AWS Cloud Service & Deployment Models

AWS (Amazon Web Services) provides a wide range of cloud services and deployment models to meet the needs of different customers. Below are the most common AWS cloud services and deployment models:

Cloud Services:

1. Compute Services: This includes services such as Amazon EC2 (Elastic Compute Cloud), AWS Lambda, and AWS Elastic Beanstalk. These services enable customers to run their applications and workloads on the cloud.

2. Storage Services: This includes services such as Amazon S3 (Simple Storage Service), Amazon EBS (Elastic Block Store), and Amazon Glacier. These services enable customers to store and retrieve data on the cloud.

3. Database Services: This includes services such as Amazon RDS (Relational Database Service), Amazon DynamoDB, and Amazon Redshift. These services enable customers to manage their databases on the cloud.

4. Networking Services: This includes services such as Amazon VPC (Virtual Private Cloud), Amazon Route 53, and Amazon CloudFront. These services enable customers to manage their networking infrastructure on the cloud.

5. Security and Identity Services: This includes services such as AWS IAM (Identity and Access Management), AWS KMS (Key Management Service), and AWS WAF (Web Application Firewall). These services enable customers to manage their security and identity on the cloud.

6. Management and Governance Services: This includes services such as AWS CloudFormation, AWS CloudTrail, and AWS Config. These services enable customers to manage and monitor their AWS resources.

7. Analytics Services: This includes services such as Amazon EMR (Elastic MapReduce), Amazon Kinesis, and Amazon Redshift. These services enable customers to perform data analytics on the cloud.

8. Application Integration Services: This includes services such as Amazon SNS (Simple Notification Service), Amazon SQS (Simple Queue Service), and Amazon SWF (Simple Workflow Service). These services enable customers to integrate their applications on the cloud.

Deployment Models:

1. Public Cloud: In a public cloud deployment model, the cloud infrastructure is owned and operated by a third-party cloud provider, such as AWS. Customers can access the cloud services over the internet.

2. Private Cloud: In a private cloud deployment model, the cloud infrastructure is owned and operated by an organization, either on-premises or in a third-party data center. The cloud services are accessed by the organization's users only.

3. Hybrid Cloud: In a hybrid cloud deployment model, the cloud infrastructure is a combination of public and private cloud resources. This model enables customers to leverage the benefits of both public and private clouds.

4. Multi-Cloud: In a multi-cloud deployment model, an organization uses multiple cloud providers, such as AWS, Azure, and Google Cloud, to meet their specific requirements.

 Create Environment with a default python version

//Crate spark context

import pyspark

from pyspark.sql import SparkSession

spark = SparkSession.builder.appName("test").getOrCreate()

//Read CSV File from a location including header as column names and schema

df = session.read.csv('archive/Case.csv', header=True,inferSchema = True)

//To show records

df.show()

//To Rename a column name

df = df.withColumnRenamed('Existing Column','new Column') 

//Select Columns

df.select('col1','col2','col3'....).show()

//sort

df.sort(''col1','col2','col3'....).show()

 //to specify asc or desc

from pySpark.sql import function as f

df.sort(f.desc( 'col1','col2','col3'....))show()

Cast

Though we don’t face it in this dataset, there might be scenarios where Pyspark reads a double as integer or string, In such cases, you can use the cast function to convert types.

 

 

sudo apt install curl

install Anaconda Python using curl

curl -O https://repo.continuum.io/archive/Anaconda3-5.0.1-Linux-x86_64.sh